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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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Supercritical fluid chromatography (SFC) provides a beneficial substitute for gas chromatography (GC) and liquid chromatography (LC) for certain samples because it merges the top attributes of both techniques. SFC allows the separation and analysis of compounds that GC or LC does not easily manage. These compounds are traditionally nonvolatile or thermally unstable, making GC unsuitable and lacking functional groups required for HPLC analysis.
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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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Interpretable Yield Prediction of Supercritical CO2 Extraction from Various Essential Oil Sources Using Optimized

Mohamed Kouider Amar1, Mohamed Hentabli1,2,3, Nabil Touzout4

  • 1Laboratory of Biomaterials and Transfer Phenomena, Theoretical and Computational Chemistry in Process Engineering Team, Faculty of Technology, University Yahia Fares of Medea, 26000 Medea, Algeria.

Journal of Chemical Information and Modeling
|December 15, 2025
PubMed
Summary

Machine learning models accurately predict essential oil yield in supercritical CO2 extraction by integrating process parameters with molecular data. This approach enhances prediction accuracy across diverse plant species and optimizes extraction efficiency.

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Area of Science:

  • Chemical Engineering
  • Computational Chemistry
  • Plant Biochemistry

Background:

  • Predicting essential oil yield in supercritical CO2 (SC-CO2) extraction is challenging due to variability in plant composition and process conditions.
  • Conventional models often fail due to assumptions of uniform feedstock behavior, limiting their use across different plant species.

Purpose of the Study:

  • To develop advanced machine learning models for predicting essential oil yield in SC-CO2 extraction.
  • To integrate extraction parameters with molecular descriptors derived from principal component analysis (PCA).
  • To enhance the accuracy and generalizability of yield prediction across diverse plant species.

Main Methods:

  • Compiled a dataset of 1313 experimental records from 42 plant species.
  • Trained LightGBM (LGBMR), HistGradientBoosting (HGBR), and Extra Trees (ETR) algorithms.
  • Optimized models using four metaheuristic algorithms and employed SHapley Additive exPlanations (SHAP) for feature importance analysis.

Main Results:

  • All models achieved high predictive performance (R² > 0.97).
  • The Extra Trees Regressor (ETR) model optimized by a genetic algorithm (ETR-3PCs-GA) showed the highest performance (R² = 0.9808).
  • The HistGradientBoosting Regressor (HGBR) model (HGBR-2PCs-GA) excelled at predicting dynamic extraction profiles (RMSE = 0.408).
  • SHAP analysis identified pressure and PCA coordinates as key features, indicating joint influence of process parameters and molecular composition.

Conclusions:

  • Integrating molecular-level information with process data provides transferable and interpretable models for SC-CO2 extraction.
  • The entire molecular profile, not just major compounds, synergistically influences essential oil yield.
  • The developed models successfully generalize yield prediction across species and confirm known process trends.